Nutrient patterns in relation to insulin resistance and endothelial dysfunction in Iranian women

Prior studies have mainly focused on the association of one specific nutrient with insulin resistance (IR) and endothelial dysfunction and limited studies have assessed the association with different nutrient patterns (NPs). We examined the association between various NPs and IR and endothelial dysfunction among Iranian women. This cross-sectional study was carried out on a sample of 368 female nurses. A 106-items food frequency questionnaire (FFQ) was applied for dietary assessments. Using factor analysis, the relationships between NPs and markers of insulin resistance (HOMA-IR, HOMA-β, and QUICKY), and endothelial dysfunction (E-selectin, sICAM-1, and sVCAM-1) were assessed. Mean age and body mass index of participants were respectively 35.21 years and 24.04 kg/m2. Three major NPs were identified. NP1, named as “dairy, fruits, and vegetables” had high values of potassium, folate, vitamins A and C, magnesium, and beta carotene. No significant association was observed between this NP and insulin resistance or endothelial dysfunction indices. The second NP was full of chromium, selenium, copper, vitamin B6, monounsaturated fatty acid (MUFA), thiamin, vitamin D, and iron. Adherence to NP2 (named “legumes, nuts, and protein foods”) was associated with lower values of insulin (6.8 ± 1.1 versus 8.4 ± 1.1, P = 0.01), homeostasis model assessment-Insulin resistance (HOMA-IR) (1.3 ± 0.2 versus 1.7 ± 0.2, P = 0.02), and vascular adhesion molecule 1 (VCAM-1) (444.2 ± 27.9 versus 475.8 ± 28.4, P = 0.03). However, adherence to the third NP, rich in saturated fatty acid (SFA), cholesterol, sodium, zinc, vitamin E, and B12, described as “animal fat and meat + vitamin E”, was associated with higher amounts of homeostasis model assessment-β (HOMA-β) (531.3 ± 176.2 versus 48.7 ± 179.8, P = 0.03). In conclusion, following the NP2, correlated with higher intakes of chromium, selenium, copper, vitamin B6, MUFA and thiamin was associated with lower values of insulin, HOMA-IR, and sVCAM-1. Adherence to NP3, rich in SFA, cholesterol, vitamin E, vitamin B12, and zinc was associated with higher levels of HOMA-β.


Assessment of biomarkers
Fasting blood samples were collected for measurement of serum concentration of insulin, blood glucose, and adhesion molecules including E-selectin, soluble intercellular adhesion molecule (sICAM-1), and soluble vascular adhesion molecule 1 (sVCAM-1).These blood samples were centrifuged for 30-45 min after collection.Then, serums were kept at − 80 to be used for the analysis.We used available commercial kits by ELISA method (Biosource International and Bender Med Systems) for assessment of sICAM-1 (nearest to 0.6 mg/dL), sVCAM-1 (nearest to 2.3 mg/dL), and E-selectin (nearest to 0.3 mg/dL).We measured fasting blood glucose (FBG) through the use of an enzymatic calorimetric (a method that assesses FBG through glucose oxidase activity).Serum insulin was also estimated through the ELISA method (Bender Med System).Then, we assessed insulin resistance and insulin sensitivity, through the following formulas: HOMA-IR = FBS (mmol/L) × Insulin (µmol/mL)/22.5 24 .

Assessment of other variables
Socioeconomic variables including the number of family members, educational level, residual status, number of bedrooms in their house, being a house owner, number and types of their cars, salary, and other sociodemographic properties such as age, marital status, menopause status, previous history of diseases, habits of taking medications or supplementations and smoking were assessed by using a self-administrated questionnaire.Body weight was measured by a digital scale (nearest to 0.1 kg), while subjects were shoeless and wearing light clothes.A tape measure was applied for evaluating standing status height.Then, body mass index (BMI) was calculated through the following formula: weight (in kilograms)/height (in meters) squared.The short form of the International Physical Activity Questionnaire (IPAQ) 35 was used for estimating daily physical activity in MET-hour per week.

Statistical analysis
Major nutrient patterns were extracted by performing factor analysis and entering 35 macro-and micro-nutrients in the analysis; these 35 nutrients were determined based on some previous publications in this regard 24,36,37 .Kaiser-Meyer-Olkin (KMO) test was applied to find out if the distribution of nutrients could be strong enough to use principal components.Factors with eigenvalues > 2 were considered as significant to extract major nutrient patterns.Scree plot was also used to identify the main nutrient patterns.Varimax rotation was conducted to extract independent nutrient patterns.Continuous and categorical characteristics of subjects were classified across tertiles of each nutrient pattern through the use of one-way ANOVA and chi-square tests, respectively.Mean dietary intakes of energy, food groups, and nutrients of participants across tertiles of nutrient patterns were obtained by ANCOVA.Mean values of glycemic factors and markers of insulin resistance and endothelial function across tertiles of nutrient patterns were estimated through ANCOVA in four models.This relationship was controlled for age and energy intake in the first model.Physical activity (MET-h/week), current corticosteroids and OCP intake (yes/no), marriage status (categorical), menopausal status (yes/no), systolic blood pressure (SBP), diastolic blood pressure (DBP), and socioeconomic status (categorical) were additionally controlled in the second model.Additional adjustment for BMI was conducted in the third model.In model 4 for association of nutrient patterns and glycemic factors and insulin resistance, additional adjustment was done for endothelial indices (E-selectin, sICAM-1, and sVCAM-1).While for association of nutrient patterns and endothelial markers, further adjustment was done for blood glucose and lipid profiles including serum triglyceride, serum total cholesterol, HDL-c, and LDL-c, in model 4. P values < 0.05 were assumed as statistically significant.Linear association between tertiles of nutrient patterns and indices of insulin resistance and endothelial function was assessed by linear regression analysis in both crude and adjusted models.Version 26 of SPSS was applied to perform all analysis.

Ethical approval and consent to participate
All participants provided an informed written consent.

Results
The current study was conducted on 368 female nurses working in Iran hospitals.The mean age and BMI of participants were respectively 35.21 years and 24.04 kg/m 2 .Three nutrient dietary patterns have been extracted through factor analysis (Fig. 1).Factor loadings of each single nutrient in each nutrient pattern are provided in Table 1.Overall, 78.5% of all dietary changes have been explained through these three nutrient patterns.Nutrient pattern 1 was associated with greater amounts of potassium, folate, vitamin A, vitamin C, magnesium, beta carotene, pantothenic acid, sugar, phosphorus, riboflavin, biotin, vitamin K, calcium, and carbohydrate.This pattern has been supposed to be rich in dairy products, fruits, and vegetables.The second nutrient pattern was correlated with higher intakes of chromium, selenium, copper, vitamin B6, monounsaturated fatty acid (MUFA), thiamin, polyunsaturated fatty acid (PUFA), vitamin D, iron, and dietary fiber.This nutrient pattern was considered to be full of legumes, nuts, and protein foods.The third nutrient pattern was related to higher values of saturated fatty acid (SFA), cholesterol, vitamin E, sodium, vitamin B12, zinc, and protein.Therefore, this NP seemed to be correlated with higher consumption of animal fat and meat + vitamin E. General features of the study subjects across tertiles of nutrient patterns are shown in Table 2.There was no significant difference in socio-demographic characteristics across tertiles of nutrient patterns 1 and 2. However, a marginally lower BMI (23.4 vs. 24.4,P = 0.05) and waist circumferences (79.1 vs. 82.1,P = 0.05) have been observed among subjects in the highest tertiles in comparison to those in the lowest tertile of NP3.Participants with menopause status were lower in the highest tertile compared to the lowest tertile of NP3 (2.2% vs. 10.7%,P = 0.01).Other socio-demographic characteristics were not significantly different between tertiles of NP3.
Multivariable-adjusted mean ± SE of glycemic indices and insulin resistance markers across tertiles of nutrient patterns are reported in Table 4.The indices of glycemic profile and insulin resistance were not significantly different across tertiles of NP1.Subjects in the highest tertile of NP2 had significantly lower insulin levels (6.8 ± 1.1 vs. 8.4 ± 1.1, P = 0.006) in comparison to the lowest tertile in fully-adjusted model.Participants in the top tertile of NP2 compared with the bottom tertile had lower levels of HOMA-IR (1.3 ± 0.2 vs. 1.7 ± 0.2, P = 0.02), in the fully-adjusted model.Other glycemic indices were not significantly different across tertiles of NP2.Subjects in the highest tertile of NP3 in comparison to the lowest tertile, had higher levels of HOMA-β (542.0 ± 176.0 vs. 44.1 ± 175.0, P = 0.03), in the second model.This association was significant even after adjustment for all potential covariates (531.3 ± 176.2 vs. 48.7 ± 179.8, P = 0.03).
Table 5 shows the multivariable-adjusted mean ± SE of endothelial function markers across tertiles of nutrient patterns.Individuals in the highest tertile in comparison to those in the lowest tertile of NP1 had higher levels of www.nature.com/scientificreports/sICAM-1 in the crude model (223.7 ± 8.5 vs. 201.1 ± 6.4, P = 0.03).This significant difference disappeared after adjustment for all covariates in model 4. In the crude model, levels of E-selectin were lower in the highest tertile compared with the lowest tertile of NP2 (79.6 ± 3.1 vs. 98.6 ± 7.8, P = 0.01).However, there was no significant difference in E-selectin levels across tertiles of NP2, after controlling for potential covariates (84.9 ± 6.4 vs. 82.0± 6.3, P = 0.94).Individuals in the highest tertile of NP2 had also lower levels of sVCAM-1 in comparison to the lowest tertile, after adjusting for all potential variables (444.2 ± 27.9 vs. 475.8± 28.4,P = 0.03).Indices of endothelial function were not significantly different across tertiles of NP3, in both crude and fully-adjusted model.The linear associations of dietary nutrient patterns with insulin resistance and endothelial function indices are reported in Table 6.A significant increase in values of sICAM-1 was seen along with each one increase in tertiles of NP1, in the crude model (B = 11.16,0.95% CI 1. 45, 20.87).This association was also significant in model 1, after adjustment for age and energy intake (B = 21.61,0.95% CI 9.76, 33.45).However, this association disappeared after further adjustment for other potential variables.There was no linear association between NP2 and markers of insulin resistance and endothelial function.Furthermore, each increase in tertiles of NP3 was associated with a marginal increase in HOMA-IR values in model 3 (B = 0.42, 0.95% CI 0.00, 0.84).This association was removed after adjustment for endothelial function markers in model 4 (B = 0.40, 95% CI − 0.02, 0.83).Since no significant consistent association was observed between nutrient patterns and most of the indexes of both insulin resistance and endothelial dysfunction, the pathway analysis was not conducted in the current study.

Discussion
In the current cross-sectional study, we illustrated that following two nutrient patterns was associated with insulin resistance and endothelial function indices.Such that, higher adherence to NP2, which consisted of chromium, selenium, copper, vitamin B6, MUFA, thiamin, vitamin D, and iron, considered as "legumes, nuts and protein foods nutrient pattern", was associated with lower values of Insulin, HOMA-IR, and VCAM-1.Moreover, higher adherence to NP3 consisting of SFA, cholesterol, vitamin E, sodium, vitamin B12, zinc, and protein, named as "animal fat and meat + vitamin E nutrient pattern", was associated with higher values of HOMA-β.Although HOMA-β is considered as an index of beta-cell function, its increased levels have shown to be associated with impaired glucose tolerance, type 2 diabetes, and insulin resistance 38,39 .Adhering to the third nutrient pattern in the current investigation has led to higher values of HOMA-β, but resulted in a reduction in QUICKY levels, a definite indicator of insulin resistance 40,41 , although this association was not statistically significant (P = 0.19).In addition, no linear association has been observed between tertiles of nutrient patterns and levels of glycemic and endothelial indices after considering all potential variables.
Obesity is known as an important risk factor for insulin resistance and prevalent around the world 42 .It has been declared that during recent years, a significant rise in prevalence of type 2 diabetes was concerning in some countries, despite lower numbers of obesity 42,43 .On the other hand, metabolic disorders such as hypertension and abdominal obesity 44,45 are drastically associated with increased endothelial dysfunction and consequently coronary artery diseases 46 .So, it can be very important to find an effective way for managing these conditions.According to our study, following a diet rich in unsaturated fatty acids, copper, selenium, manganese, chromium, zinc, vitamin B6, thiamin, vitamin D, and dietary fiber, along with lower consumption of SFA, cholesterol, vitamin E, sodium, potassium, and vitamin B12 might help reduce risks of insulin resistance and endothelial dysfunction.More clinical trials are necessary to confirm these observations.Previous studies have estimated the association between various nutrients and IR markers.For example, a prospective cohort study on 995 subjects has suggested a reduction in IR and hyperinsulinemia by following a nutrient pattern rich in potassium, vitamins B6, C, and A 24 .Moreover, significant inverse associations were observed between adherence to the nutrient pattern rich in vitamin B and dietary fiber, and another pattern, called zinc, thiamin, and plant proteins with the values of glycated hemoglobin and fasting glucose in a prospective cohort study in South Africa 47 .Furthermore, another observational study among Iranian overweight and obese adolescents has reported an increased risk of metabolically unhealthy obesity as well as an increment in Table 2. General characteristics of study population across categories of nutrient pattern scores.Data are means ± SD or number (%).Q quartile, BMI body mass index, MET-h/wk metabolic equivalent-hour per week, OCP the oral contraceptive pill.a Obtained from analysis of variance (ANOVA) for continuous variables and chi-square for categorical variables.b High socioeconomic status was defined based on educational level, income, family size, being house owners, house area, number and kind of the car (s), number of bedrooms, and determination of who was in charge for the family.www.nature.com/scientificreports/HOMA-IR levels through following a "high fat and sodium" nutrient pattern 48 .Moreover, it has been reported that following a diet with a higher Mediterranean-style score, rich in MUFA, PUFA, nuts, and seeds in children, might be associated with lower levels of HOMA-IR, fat mass index (FMI), and cardiometabolic risk in their adulthood 49 .Another 3-year prospective cohort study has found an inverse association between higher dietary approaches to stop hypertension (DASH) score and IR.DASH score was defined by higher intakes of legumes, nuts, fruits, and vegetables, and lower intakes of sodium, red and processed meat, and sweetened beverages in the mentioned study 50 .A meta-analysis of 44 trial and prospective cohort studies on patients with diabetes has also demonstrated a reduction in HbA1C and HOMA-IR levels in higher intakes of dietary fiber 51 .These investigations might confirm the favorable effect of NP2 in the current study (named legumes, nuts, and protein food) on levels of serum insulin and HOMA-IR.On the other hand, saturated fatty acids have been proven to increase the risk of insulin resistance 52 .Higher meat consumption was associated with an increase in HOMA and insulin levels in a population of non-diabetic women 53 .It has also been claimed that diets rich in animal protein might increase insulin resistance regardless of weight 54 .So, the increments in levels of HOMA-β in the present study across tertiles of NP3 (described as the meat and animal fat pattern) could be supported by these evaluations.Several mechanisms might explain the association of nutrients with insulin resistance and endothelial dysfunction.Interventional studies have suggested that supplementation of zinc, selenium, and chromium might improve insulin resistance by reducing oxidative stress which can impair insulin secretion from β cells 55,56 .Table 4. Multivariable-adjusted glycemic profile and insulin resistance across tertiles of nutrient pattern scores.All values are means ± SE.P were obtained from analysis of covariance (ANCOVA).Q quartile, FBG fasting blood glucose, HOMA-IR homeostatic model assessment of insulin resistance, HOMA-β homeostatic model assessment of beta-cell function, QUICKI quantitative insulin sensitivity check index.a Model 1: Adjusted for age and energy intake.b Model 2: Further adjusted for physical activity (MET-h/wk), current corticoid steroids use (yes or no), current OCP use (yes or no), marital status (categorical), menopausal status (yes or no), systolic blood pressure, diastolic blood pressure, and socioeconomic status (categorical).c Model 3: Further adjusted for BMI.d Model 4: Additionally adjusted for markers of endothelial function (E-selectin, sICAM-1, and sVCAM-1).www.nature.com/scientificreports/Additionally, it has been suggested that chromium might be able to increase insulin binding through increasing the number of insulin receptors and their phosphorylation 57 .The protective role of selenium against insulin resistance and type 2 diabetes might be associated with its ability to enhance the activity of glutathione peroxidase (GPx), which defends against reactive oxygen species (ROS) 58 .A combination of vitamin D3 and chromium has also shown to decrease HOMA-IR levels by regulation of inflammatory markers like TNF-α 16 .On the other hand, MUFA consumption has a favorable effect on sVCAM-1 through the reduction in NF-kB, another marker of oxidative stress 59,60 .Co-supplementation of omega 3 fatty acids and chromium could also enhance endothelial function by preventing the activity of phospholipase A2, a prooxidant enzyme, and provoking antioxidant enzymes 61 .A randomized control trial on 124 children with type 1 diabetes documented that folate and vitamin B6 supplementation had a positive effect on endothelial function, because folate supplementation could enhance levels of tetrahydrobiopterin, a substantial cofactor for NO synthesis 62 .Furthermore, vitamin B6 could regulate the inflammatory response 63 .Vitamin D and its receptors (VDRs) could also enhance endothelial function by increasing NO synthesis, through a positive regulation in the activity of endothelial Nitric Oxide Synthase (eNOS) 64 .
As far as we know, this is the first study investigating the association of various NPs with insulin resistance and endothelial dysfunction.Moreover, validated questionnaires were used to assess dietary intakes and covariates.Nevertheless, some limitations can be acknowledged in our study.Considering cross-sectional design of the study, causal relationships could not be confirmed.Since the current investigation was conducted on a population of nurses living in Isfahan, generalizing the results to all Iranian women might not be totally possible.Although was controlled for several confounders in the analyses, the effect of residual confounders might not be avoided.In addition, misclassification and measurement errors are unavoidable due to the self-reported design of questionnaires.Finally, the study was carried out on a particular group of people (female nurses working in hospitals) and its findings could not be generalized to the whole adult population.
In conclusion, in the current cross-sectional study higher adherence to the second nutrient pattern, associated with higher intakes of chromium, selenium, copper, vitamin B6, MUFA, PUFA, vitamin D, and iron was associated with lower Insulin, HOMA-IR, and VCAM-1 values.However, higher adherence to the third nutrient pattern, rich in SFA, cholesterol, vitamin E, sodium, and vitamin B12 was associated with higher HOMA-β values.Considering the findings of the current study, adhering to a nutrient dietary pattern, rich in selenium, copper, iron, vitamin B6, vitamin D, and unsaturated fatty acids (including PUFAs and MUFAs) with lower intakes of cholesterol, sodium, vitamins E and B12, and saturated fatty acids can reduce the risk of insulin resistance and endothelial dysfunction in female population.However, further prospective investigations are required to affirm these associations.
Table 5. Multivariable-adjusted association between markers of endothelial function and tertiles of nutrient pattern scores.All values are means ± SE.P were obtained from analysis of covariance (ANCOVA).Q quartile, E-selectin endothelial selectin, sICAM-1 soluble intercellular adhesion molecule-1, sVCAM-1 soluble vascular cell adhesion molecule-1.a Model 1: Adjusted for age and energy intake.b Model 2: Further adjusted for physical activity (MET-h/wk), current corticoid steroids use (yes or no), current OCP use (yes or no), marital status (categorical), menopausal status (yes or no), systolic blood pressure, diastolic blood pressure, and socioeconomic status (categorical).c Model 3: Further adjusted for BMI.d Model 4: Additionally adjusted for blood lipids (serum triglyceride, serum total cholesterol, HDL, and LDL-cholesterol) and glucose.

Figure 1 .
Figure 1.Scree plot for identifying major nutrient patterns in Iranian women.

Table 1 .
Factor loadings and explained variances for major nutrient patterns (NPs).Factor loadings < │0.20│ are not shown for simplicity.The Kaiser-Meyer-Olkin value was 0.85.Retained factors with Eigenvalues ≥ 2 were extracted as major NPs.

Table 3 .
Dietary intakes of study participants across tertiles of nutrient patterns.a Dietary intakes of foods and nutrients are reported.All values are means ± SE.Q quartile, MUFA monounsaturated fatty acid, PUFA polyunsaturated fatty acid.

Table 6 .
Linear association of nutrient dietary patterns 1 with insulin resistance and endothelial function indexes.All values are linear regression coefficient and 95% CIs, In tertiles, as continuous variables.P was obtained from linear regression analysis.